| --- |
| license: apache-2.0 |
| base_model: Akashpb13/Swahili_xlsr |
| tags: |
| - generated_from_trainer |
| datasets: |
| - ml-superb-subset |
| metrics: |
| - wer |
| model-index: |
| - name: xho_finetune |
| results: |
| - task: |
| name: Automatic Speech Recognition |
| type: automatic-speech-recognition |
| dataset: |
| name: ml-superb-subset |
| type: ml-superb-subset |
| config: xho |
| split: test |
| args: xho |
| metrics: |
| - name: Wer |
| type: wer |
| value: 53.510895883777245 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # xho_finetune |
| |
| This model is a fine-tuned version of [Akashpb13/Swahili_xlsr](https://huggingface.co/Akashpb13/Swahili_xlsr) on the ml-superb-subset dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5370 |
| - Wer: 53.5109 |
| |
| ## Model description |
| |
| More information needed |
| |
| ## Intended uses & limitations |
| |
| More information needed |
| |
| ## Training and evaluation data |
| |
| More information needed |
| |
| ## Training procedure |
| |
| ### Training hyperparameters |
| |
| The following hyperparameters were used during training: |
| - learning_rate: 9.6e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 64 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 25 |
| - training_steps: 500 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Wer | |
| |:-------------:|:-------:|:----:|:---------------:|:-------:| |
| | 25.5184 | 0.7692 | 10 | 24.2275 | 100.0 | |
| | 14.5363 | 1.5385 | 20 | 9.8357 | 100.0 | |
| | 4.5811 | 2.3077 | 30 | 3.8367 | 100.0 | |
| | 3.4822 | 3.0769 | 40 | 3.3922 | 100.0 | |
| | 3.2732 | 3.8462 | 50 | 3.2398 | 100.0 | |
| | 3.1796 | 4.6154 | 60 | 3.1705 | 100.0 | |
| | 3.1504 | 5.3846 | 70 | 3.1419 | 100.0 | |
| | 3.1119 | 6.1538 | 80 | 3.1084 | 100.0 | |
| | 3.0789 | 6.9231 | 90 | 3.0735 | 100.0 | |
| | 3.0619 | 7.6923 | 100 | 3.0590 | 100.0 | |
| | 3.0298 | 8.4615 | 110 | 3.0247 | 100.0 | |
| | 2.9933 | 9.2308 | 120 | 2.9716 | 100.0 | |
| | 2.9079 | 10.0 | 130 | 2.8647 | 100.0 | |
| | 2.8414 | 10.7692 | 140 | 2.7931 | 100.0 | |
| | 2.6939 | 11.5385 | 150 | 2.5932 | 100.0 | |
| | 2.3274 | 12.3077 | 160 | 2.1000 | 99.7579 | |
| | 1.7068 | 13.0769 | 170 | 1.4580 | 93.4625 | |
| | 1.206 | 13.8462 | 180 | 1.1027 | 83.0508 | |
| | 0.9587 | 14.6154 | 190 | 0.9152 | 79.4189 | |
| | 0.7806 | 15.3846 | 200 | 0.8122 | 69.7337 | |
| | 0.7118 | 16.1538 | 210 | 0.7445 | 69.0073 | |
| | 0.6814 | 16.9231 | 220 | 0.6945 | 62.9540 | |
| | 0.5709 | 17.6923 | 230 | 0.6787 | 67.5545 | |
| | 0.5653 | 18.4615 | 240 | 0.6758 | 62.2276 | |
| | 0.5437 | 19.2308 | 250 | 0.6511 | 60.7748 | |
| | 0.5092 | 20.0 | 260 | 0.6237 | 62.7119 | |
| | 0.4239 | 20.7692 | 270 | 0.6000 | 61.5012 | |
| | 0.4355 | 21.5385 | 280 | 0.5899 | 59.8063 | |
| | 0.4456 | 22.3077 | 290 | 0.5960 | 59.3220 | |
| | 0.3986 | 23.0769 | 300 | 0.5764 | 56.6586 | |
| | 0.3856 | 23.8462 | 310 | 0.5801 | 55.9322 | |
| | 0.3607 | 24.6154 | 320 | 0.5682 | 57.6271 | |
| | 0.358 | 25.3846 | 330 | 0.5675 | 55.9322 | |
| | 0.3452 | 26.1538 | 340 | 0.5630 | 57.8692 | |
| | 0.3289 | 26.9231 | 350 | 0.5515 | 57.8692 | |
| | 0.353 | 27.6923 | 360 | 0.5621 | 57.3850 | |
| | 0.2907 | 28.4615 | 370 | 0.5486 | 55.2058 | |
| | 0.3237 | 29.2308 | 380 | 0.5445 | 54.4794 | |
| | 0.3202 | 30.0 | 390 | 0.5384 | 52.7845 | |
| | 0.2918 | 30.7692 | 400 | 0.5370 | 55.6901 | |
| | 0.3106 | 31.5385 | 410 | 0.5422 | 53.7530 | |
| | 0.3105 | 32.3077 | 420 | 0.5438 | 55.2058 | |
| | 0.2835 | 33.0769 | 430 | 0.5437 | 55.9322 | |
| | 0.2966 | 33.8462 | 440 | 0.5416 | 54.7215 | |
| | 0.2719 | 34.6154 | 450 | 0.5394 | 54.2373 | |
| | 0.2859 | 35.3846 | 460 | 0.5384 | 53.7530 | |
| | 0.29 | 36.1538 | 470 | 0.5379 | 53.2688 | |
| | 0.2879 | 36.9231 | 480 | 0.5372 | 53.5109 | |
| | 0.2871 | 37.6923 | 490 | 0.5370 | 53.5109 | |
| | 0.3019 | 38.4615 | 500 | 0.5370 | 53.5109 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.41.1 |
| - Pytorch 2.3.0+cu121 |
| - Datasets 2.19.1 |
| - Tokenizers 0.19.1 |
| |
